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Urban Development Scenarios

Urban Development Scenarios. Urban development scenarios. Outline the strategy for the designing of the scenarios Discuss relevant urban trends and driving forces behind the urban changes (including policies)

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Urban Development Scenarios

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  1. Urban Development Scenarios

  2. Urban development scenarios • Outline the strategy for the designing of the scenarios • Discuss relevant urban trends and driving forces behind the urban changes (including policies) • Identify, on the basis of the previous analysis, a first proposal for the interval of change of the variables of interest

  3. Scenarios • Changes in a set of external driving forces and policy decisions (economic changes) • Translation into model input variables and parameters (OD Matrix)

  4. Scenarios: strategy • Common scenarios • comparison • allowing general recommendation to be drawn • City-specific scenarios • realism • allowing specific questions to be solved

  5. Baseline and alternative scenarios • Current situation as a baseline • WP11 will define an interval of values for the variables of interest • extremes of the interval -> common scenarios • within the interval -> specific scenarios

  6. Issues to be covered • Relevant urban issues • demographic changes • economic structure changes • technological changes • land-use changes • Each set of scenarios is defined in terms of changes of one single parameter • Assumptions are made to collapse all relevant variables into that particular parameter

  7. Summary of scenarios ISSUES ADDRESSED Baseline Upper end Lower end Demographic changes The city is growing and The city is shrinking and getting younger getting older Economic structural The city is changing fast The city is changing slowly changes towards a high-tech service- towards a high-tech service- based economy based economy Technological changes The city is moving fast The city is moving slowly Current situation towards improving transport towards improving transport efficiency efficiency Land-use changes The city is densifying and The city is sprawling and mixing land uses separating land uses

  8. Urban development scenarios • Outline the strategy for the designing of the scenarios • Discuss relevant urban trends and driving forces behind the urban changes (including policies) • Identify, on the basis of the previous analysis, a first proposal for the interval of change of the variable of interest

  9. Next steps • Finalisation of the definition of the interval for the variables of interest • Identification of city-specific scenarios • Modelling strategy: translation into model input variables and parameters (scaling of OD matrix)

  10. Demographic changes • Issues to be covered • Changes in the size of the population • More people means more travel • Changes in the age structure of the population • Pensioners have different transport requirements from employees • Relevant indicators • number of inhabitants • percentage of population  18 • percentage of population  64

  11. Recent urban trends 1.5 1 Stockholm Oslo Helsinki Strasbourg Utrecht Malmoe Thessaloniki Amsterdam Wien Hamburg 0.5 Paris London Rotterdam Population growth over 1990-98 Napoli Kopenavhn Madrid Antwerpen Lazio Milano Bremen Lille Barcelona 0 Birmingham Athina Bruxelles Liverpool Genova Bilbao -0.5 Lisboa -1 -1 -0.5 0 0.5 1 1.5 Population growth over 1980-89

  12. Driving forces • Mortality rate • falling in many EU countries because of increasing hygiene and health standards and medical research progress • not big difference between urban centres and overall population • Natality rate • falling in many EU countries, in some it shows a rebate due to higher natality rate in immigrant population • not big differences between urban centres and overall population • Net migration

  13. Natality and mortality rates

  14. Natural growth- in the 5 fastest growing cities Strasbourg Thessaloniki Stockholm Helsinki Utrecht 5 3 Contribution to growth (%) 1 -1 1980 1998

  15. Natural growth- in the 5 slowest growing cities Genova Liverpool Bruxelles Bremen 5 Birmingham 3 1 -1 -3 Contribution to growth 1980 1998

  16. Net migration- in the 5 fastest growing cities 8 6 Strasbourg 4 Thessaloniki Contribution to growth (%) Stockholm Helsinki 2 Utrecht 0 -2 1998 1980

  17. Net migration- in the 5 slowest growing cities 8 6 4 Genova Liverpool 2 Bruxelles Bremen Birmingham 0 -2 -4 Contribution to growth 1980 1998

  18. Determination of the interval • Low growth scenarios: –1% pa (implying a population in 2030 equal to 74% of population in 2000). • The low growth scenarios assumes negative natural and migratory balances (each contributing some –0.5%pa) • High growth scenarios: +1.5% pa (implying a population in 2030 equal to 156% of population in 2000). • The high growth scenarios assumes positive natural (+0.5% pa) and migratory balances (+1% pa).

  19. An alternative interval • Low growth scenarios: -5% pa (implying a population in 2030 equal to 21% the population in 2030) • negative contribution of the natural (-2% pa) and migration balance (–3% pa). • Oumigration as in Liverpool and Birmingham in the 1980s and very low fertility rates as in Genova. • High-growth scenarios:+5% pa (implying a population in 2030 equal to 432% the population in 2000) • positive contribution of the natural (+2% pa) and migration balances (+3%pa). • favorable external and internal conditions as in Thessaloniki in the 1990s and very high fertility rates as in Napoli in the 1980s

  20. Population growth and age structure France Working age Youth Pensioners Spain Working age Youth Pensioners

  21. UN population growth forecasts - pensioners 15 12 9 6 3 0 -1 -0.5 0 0.5 1 1.5 Spain Austria Poland Italy Switzerland Greece Sweden Percentage change in total population share over 30 years (pp) Germany Portugal Finland Netherlands Belgium United Kingdom Ireland France Denmark Israel Argentina Luxembourg Population growth (% pa) Source: UN

  22. UN population growth forecasts - working age 0 -3 -6 -9 -12 -1 -0.5 0 0.5 1 1.5 Argentina Israel Luxembourg Denmark United Kingdom Percentage change in total population share (pp) France Ireland Belgium Netherlands Sweden Finland Germany Portugal Switzerland Austria Greece Italy Poland Population growth (% pa) Source: UN

  23. Definition of the interval • Low-growth scenarios • youth share decreases by 5pp, working age decreases by 10pp, old age pensioners share increases by 15pp • High-growth scenarios • youth share increases by 0pp, working age decreases by 3pp, old age pensioners share increases by 3pp

  24. Economic changes • Towards a high-tech service-based economy • Issues to be covered • Shift to services • services have different transport requirements with respect to manufacturing (passengers vs. freight, frequency of trips) • Shift towards ICT-enabled teleworking • teleworkers have different transport requirements from standard commuters (less work trip, more leisure trips) • Relevant indicators • percentage of employment in the service sector • percentage of employment on teleworking

  25. Service employment: trends at EU and urban level

  26. Convergence in service employment 20 15 10 5 0 20 40 60 80 100 Thessaloniki Lille Liverpool Napoli Milano Birmingham Rotterdam Wien Athini AV EUR Strasbourg Genova Bilbao Change between 1980 and 1998 (pp) Hannover Roma London Utrecht AV CITIES Paris Amsterdam Barcelona Helsinki Hamburg Antwerpen Madrid Bremen Lisboa Bruxelles Oslo Malmoe Kopenavhn Stockholm Service share in 1980 (%)

  27. Driving forces • Rise of income • Lagging productivity in services • GDP per capita and lagging productivity in services appears to be the main determinant of service share in total employment. • Exogenous shift in the demand for services • female participation rate (+) • size of the welfare state (+) • stricter employment protection (-) • the average tax wedge, product market regulation and earning compressions (0)

  28. Teleworking • Home based-teleworkers • one full day per week • Occasional teleworkers • less than one full day per week • In Europe (ECaTT, % of total labour force) • 1999. 6.1% teleworkers, 2% home-based • 2005. 10.8% teleworkers, 4.2% home-based • High variety of situations • Finland home-based, from 6.7% to 16.7% • Spain home-based from 1.3% to 2.7%

  29. Driving forces and barriers • Driving forces • ICT development • development in services that are produced and delivered electronically • Barriers to teleworking data security problem costs for technological equipment * less important than in the 1994 survey productivity and work quality problems organising communication * less important than in the 1994 survey insufficient knowledge managers health, safety, insurance and legal problems difficulties managing teleworkers employees would not want lack of pressure for change resistance from trade unions * less important than in the 1980s * more important in France, Spain and Italy

  30. Construction of the interval • Fast-change scenarios • share of service employment increases by 20 pp over 2000-2030; share of home-based teleworking to 70% in 2030. • in this scenarios, the service share of employment in some cities will reach 100%. • Slow-change scenarios • share of service employment increases by 5pp over 2000-2030; share of home-based teleworking to 15% in 2030.

  31. Land use changes • Issues to be covered • Urban sprawl • densification of less-developed areas and expansion at the urban fringe • Mixed land use • locates land uses with complementary functions close together • Relevant indicators • structural density • index of mixed land use

  32. Recent urban trends* Distribution of population • EEA, 1999, speaks of “increasing dispersal and sprawling of urban settlements with declining urban population densities and greater requirements for urban infrastructure” • This is a very robust finding in both industrial and developing countries and apply to cities as diverse as Bangkok, Bogota, Mexico City, Shangai and Tokyo (Ingram, 1998) • In the US, from one-third to one-half of central location in large cities have lost population over the past 25 years

  33. Recent urban trends* Distribution of employment • Employment tend to decentralise • But it is more centralised than the population • Manufacturing is less centralised than service employment • Among services, retail activities tend to disperse and being replaced by employment in finance, law and other activities • in the US, retail is now widely decentralised

  34. Driving forces * Distribution of population • Lower land prices and lower development costs in the periphery • Wider availability of motorised modes of transport • Increasing share of service jobs • Increasing homogeneity of lifestyles in the country and urban areas • ICT development has not clear impact

  35. Driving forces * Distribution of employment • Employment decentralisation can be seen as a move to improve job-housing balance • Manufacturing is attracted to the fringe by the lower transport and land costs • Services are attracted to the centre by the implicit need for close communication and face-to-face interaction • Retail services are attracted to the fringe by easier accessibility and lower land costs

  36. Construction of the interval • Sprawling scenarios • positive population growth • city area increases proportionately • negative population growth • city area do not change • three circles: manufacturing and retail services, residential places, service jobs • Mixed-use scenarios • positive population growth • city area do not change • negative population growth • city area decreases proportionately • proportional distribution of manufacturing and retail services, residential places, service jobs

  37. Urban Development Scenarios • TECHNOLOGICAL SCENARIOS • This set of scenarios considers changes caused by technological improvements for road traffic enhancing efficiency in the use of traffic supply and curbing road emissions

  38. Technological changes • Levers to be applied • Passenger car occupancy rate • Modal share: public and private transport • Information Technology in traffic control & management • Penetration rates of new technologies • These parameters take into account the most likely and important technological changes to be addressed in road transport scenarios according to a feasible implementation in SUTRA software.

  39. Technological scenariosPassenger car occupancy rate • Driving Forces Occupancy rate is strongly related to: • Tightening of local traffic regulations - car pooling, car sharing, High Occupancy Vehicles Lanes, road pricing • Increment in number of car bought per person - rise of income • Increase of singles specially in large cities • Passengers attitude towards more freedom in transport

  40. Technological scenariosPassenger car occupancy rate • Past trends (Unit: passenger per car) Source: Auto Oil Programme II - Base Case

  41. Technological scenariosPassenger car occupancy rate • Future trends Peak occupancy rate is expected to be constant in time. A slight decrease of less than 1% is envisaged in 30 years for peaks and a decrement of less than 5% for average occupancy rates. Source: Elaboration from AOPII Cost-effectiveness Study Part III: The Transport Base Case

  42. Technological scenariosModal share: public and private transport • Road transport categories * Taxi belongs to public transport, but it works and has effects like private transport, and therefore it has been treated as a part of private transport

  43. Technological scenariosModal share: public and private transport • Drivingforces The modal shares are strictly related to: • Private transport regulations (road pricing, park pricing, fuel taxes) • Public transport capability and efficiency • Passenger behaviour (need for freedom in transport Vs public transport cost-effectiveness) • Future trends Public shares are quite constant in time, variations are less than 10% in 30 years and trends slightly vary both decreasing or increasing (Auto Oil Programme II -The Transport Base Case)

  44. Technological scenariosModal share: public and private transport • Past and future trends * For each city five shares are presented referring to the years 1990, 2000, 10, 20, 30. Source: Elaboration from AOPII Cost-effectiveness Study Part III: The Transport Base Case

  45. Technological scenariosInformation Technology • Past and future trends At present there are no realistic quantitative forecasts about the impact of telematics on transport flows in terms of traffic data in 2020 or even 2010 perspectives. Although some studies highlight a constant and fast evolution on the telematics sector. (European Commission, SCENARIOS C3, 1999). Forecast for 2010 envisage that 90% of cars will be equipped with systems for Advanced Transport Telematics (Wahl, 1998).

  46. Technological scenariosInformation Technology • In an optimistic scenario it can be assumed that IT in traffic control will be fully used in 2030 providing travellers with complete information (congestion, accidents, parking). • In SUTRA scenarios the application of Advanced Transport Telematics will be simulated in the transport model using two different assignment methods for private transport route selection. • The low case scenario implements - partial and increasing knowledge on traffic condition- learning method assignment • The high case, referring to a complete knowledge on traffic condition, implements the equilibrium method.

  47. Technological scenariosPenetration rate of new technologies • The considered new technologies are: Electric Vehicle, Hybrid Electric Vehicle, Fuel Cell Electric Vehicle. • Driving forces • Local and national policies options • Economic incentives for cleaner vehicles • Increasing taxes on traditional fuel • Market driven mechanisms • Costs for producing cleaner technology vehicles • Purchaser attitude towards environmental friendly vehicles

  48. Technological scenariosPenetration rate of new technologies • Future trends * EV: Electric Vehicle; HEV: Hybrid electric vehicle; FCEV: Fuel Cell Electric Vehicle Source: Elaboration from COST 319 Action Programme

  49. Technological scenarios • Technical scenarios summary

  50. Technological scenariosModal share: public and private transport • Road transport categories * Taxi belongs to public transport, but it works and has effects like private transport, and therefore it has been treated as a part of private transport

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